Gap Junctions and Epileptic Seizures – Two Sides of the Same Coin?

Affiliations
Computational Neurobiology Laboratory, The
Salk Institute for Biological Studies, La Jolla, California, United States of
America,
Center for Theoretical Biological Physics,
University of California San Diego, La Jolla, California, United States of
America

Affiliations
Computational Neurobiology Laboratory, The
Salk Institute for Biological Studies, La Jolla, California, United States of
America,
Department of Cell Biology and Neuroscience,
University of California Riverside, Riverside, California, United States of
America

Gap Junctions and Epileptic Seizures – Two Sides of the Same Coin?

Figures

Abstract

Electrical synapses (gap junctions) play a pivotal role in the synchronization of
neuronal ensembles which also makes them likely agonists of pathological brain
activity. Although large body of experimental data and theoretical
considerations indicate that coupling neurons by electrical synapses promotes
synchronous activity (and thus is potentially epileptogenic), some recent
evidence questions the hypothesis of gap junctions being among purely
epileptogenic factors. In particular, an expression of inter-neuronal gap
junctions is often found to be higher after the experimentally induced seizures
than before. Here we used a computational modeling approach to address the role
of neuronal gap junctions in shaping the stability of a network to perturbations
that are often associated with the onset of epileptic seizures. We show that
under some circumstances, the addition of gap junctions can increase the
dynamical stability of a network and thus suppress the collective electrical
activity associated with seizures. This implies that the experimentally observed
post-seizure additions of gap junctions could serve to prevent further
escalations, suggesting furthermore that they are a consequence of an adaptive
response of the neuronal network to the pathological activity. However, if the
seizures are strong and persistent, our model predicts the existence of a
critical tipping point after which additional gap junctions no longer suppress
but strongly facilitate the escalation of epileptic seizures. Our results thus
reveal a complex role of electrical coupling in relation to epileptiform events.
Which dynamic scenario (seizure suppression or seizure escalation) is ultimately
adopted by the network depends critically on the strength and duration of
seizures, in turn emphasizing the importance of temporal and causal aspects when
linking gap junctions with epilepsy.

Funding: This research was supported in part by the National Institutes of Health (NIH)
grant R01 NS059740 (MB and VV). VV acknowledges support from the NSF-sponsored
Center for Theoretical Biological Physics at the University of California San
Diego. MP acknowledges support from the Slovenian Research Agency (grant number
Z1-2032). The funders had no role in study design, data collection and analysis,
decision to publish, or preparation of the manuscript. No additional external
funding was received for this study.

Competing interests: Matjaž Perc is an Academic Editor at PLoS ONE. The authors have declared
that no competing interests exist.

Introduction

Most of the communication between the neurons in the brain of an adult animal is
achieved by means of chemical synapses [1]. In the cortex for example, a
typical neuron can get as many as synaptic connections
from, and communicate its spike to, other neurons, thus making synaptic transmission
a ubiquitous mode of information transfer. Consequently, over the years many
successful “synaptic theories” were developed to explain a variety of
phenomena, such as the working memory [2], long-term plasticity and
memory formation [3], but also pathological conditions such as the
schizophrenia [4] and the epilepsies [5]. In particular, one of the
widely accepted theories attributes epileptic seizures to the shift of synaptic
balance toward excitation in conditions of impaled inhibition or augmented
excitation [5]–[7].

Besides chemical synapses, neurons can also form direct electrotonic connections with
their peers via electrical synapses, or so called gap junctions [8], [9]. Conceptually, a
network of neurons coupled by gap junctions has often been likened to excitable
reaction-diffusion (RD) media [10]. In RD systems, a substance (usually chemical) spreads by
diffusion from one excitable site to its neighbors where it can be regenerated by a
reaction, and the process repeats itself, resulting in the propagation of
regenerative waves through the medium [11], [12]. The speed of wave propagation
is mostly limited by the characteristic reaction time. In RD neuronal networks,
membrane voltage plays the role of a “diffusible chemical” and gap
junctions play a role of “diffusion” providing coupling of membrane
voltages between neighbor neurons; since the spike width (“reaction
time”) is very short (∼ 1 ms) the “voltage wave” can quickly
engage neurons that are relatively far apart in space. Indeed, computational
modeling studies supported by mathematical analysis suggested that a network of
neurons coupled by gap junctions can support collective activity in the form of
waves that are generated spontaneously and propagate through the network [13]. The existence
of such dynamical state requires the coupling by gap junctions to be sparse and
strong [13]. Gap
junctions also play an important role in promoting synchronization in networks of
inhibitory interneurons, which is believed to be necessary for the generation of
collective oscillatory activity in the gamma band (30–80 Hz) [14], [15].

The role of gap junctions in synchronization of neuronal ensembles and propagation of
neuronal activity led to suggest that this mode of communication could be a critical
factor in the emergence of some forms of pathological activity, such as the
epileptic seizures, as reviewed by Perez-Velazquez and Carlen [16]. The emergence of
epileptic activity in the brain usually reflects an imbalance between depolarizing
influences (which tend to excite the neuron and result in more intense spiking) and
hyperpolarizing influences (which suppress spiking activity by keeping the membrane
potential sufficiently below the threshold for spike generation). Any change in the
excitation-inhibition balance that favors excitation can in principle promote
seizure-like activity [17], [18]. Because gap junctions enhance synchronization in
networks of neurons, this can potentially lead to the elevation of excitatory
activity and thus promote epileptogenesis. The hypothesis about the putative role of
neuronal gap junctions in seizures is further supported by the observation that the
expression of gap junctions is enhanced in epileptic slices.

However alluring is the hypothesis linking neuronal gap junctions to epileptic
seizures, there remain several caveats. Firstly, in experimental models the
expression of gap junctions is significantly increased following
the epileptic-like intense stimulation of otherwise “normal” slice [19], [20]. This often
overlooked fact puts a question mark on the causal role of direct electrical
coupling in seizure onset. If addition of gap junctions always promotes seizures,
then why does the network respond to the imposed seizure-like stimulation by adding
more gap junctions? Secondly, the expression of neuronal gap junctions is highest in
early development, progressively decreases as synaptic connectivity patterns are
established, but is not reduced to zero even in mature and “healthy”
networks [9], [21], as would be
expected if gap junctions played a purely pathological role in shaping network
dynamics. This suggests that, at least in some cases, an abundance of gap junctions
could represent an adaptive response of a network to aberrant patterns of overly
synchronized and intense activity (as occurs in immature networks) rather than being
an anatomical aberration in itself. Finally, computational models of
reaction-diffusion systems usually assume sparse, strong, and topologically regular
connectivity which promotes synchronization and wave propagation. However, in real
gap-junction coupled neuronal networks, both the number of neurons to which a given
neuron connects via electrical synapses, and the strength of this connection
(unitary conductance) can vary greatly across the network [8], [22]. Hence, one must be careful in
carrying the results derived for “classical” RD systems over to gap
junction coupled neuronal networks with heterogeneous patterns of electrical
connectivity.

Here, we have devised a minimal biophysically plausible computational model of
neuronal network in order to investigate the role of neuronal gap junctions in
maintaining stability in network dynamics. We show that for physiological levels of
spiking activity that are typical for a “healthy” cortex (∼5 Hz for
pyramidal neurons and ∼10 Hz for interneurons), gap junctions can serve as a
mechanism that would stabilize neuronal network dynamics in response to relatively
mild perturbations in neural activity. This stabilizing effect relies critically on
the topological connectivity, strength of individual gap junction connection, and
strength of the perturbation to neural activity; with strong perturbations, gap
junctions promote collective high-frequency oscillations indicative of epileptic
activity. By topological connectivity we mean here the topology of the
“connectivity space”, which defines the properties of signal (or
perturbation) propagation through the network of connected neurons. Based on these
observations, we propose a solution to the apparent paradox regarding the enhanced
expression of gap junctions that follows seizure-like stimulation [19], [20]. Using
model simulations, we show that initial post-seizure addition of gap junctions can
suppress the pathological seizure-like excitation of the neuronal ensemble. This
suggests that an experimentally observed increased post-seizure expression of gap
junction channels could represent an adaptive response of the neuronal network to
the potentially pathological perturbation of activity. If the activity perturbation
is relatively weak and transient, such adaptive responses can suppress seizure
initiation. Conversely, if the perturbation is strong and persistent, an initially
activity-suppressing effect of enhanced gap junctional connectivity can revert and
lead to seizure escalation (with gap junctions engaging neurons in synchronous
firing). Our results thus imply that gap junctions could be either deleterious or
beneficial, depending on the strength and the duration of the applied perturbation.
Thus, we suggest that the hypothesis linking gap junctions to epileptic seizures
should be revised to account for temporal and causal aspects.

Results

We considered 2D networks of 50×50 model neurons coupled with gap
junctions, with periodic boundary conditions (Figure 1). Neuronal dynamics were described
using the well-studied Morris-Lecar model [23] that was slightly modified
by Prescott et al. [24] to account for correct biological mechanisms of
action potential generation. This simplified model provides an optimal balance
between realistic electrical properties of neuron and computational performance
that allows simulations of large-scale 2D networks. In the baseline model, each
model neuron was coupled by gap junctions to its Z nearest neighbors (Z ranged
from 4 to 24, as described in Methods); pattern of gap junction connectivity
determined properties of spike propagation through the network (Figure 1C). Random (see
Methods for details) external input with amplitude determined by parameter
Dn was applied to all the neurons to drive
them beyond the spiking threshold. This external stimulation can be interpreted
as a random synaptic input from the rest of the neuronal population that was not
included in this network model.

A Schematic presentation of network connectivity, for
Z = 4 (four gap junction connections per model
neuron). B Intensity of stimulation current determines the
rate of neuronal spiking. Top panel: . Middle
panel: . Bottom
panel: . These
traces of membrane potential are for an isolated model neuron (not
connected to other model neurons). C Strong topological
coupling by gap junctions can impede signal transmission. Model neuron 1
was stimulated by a brief step-like constant current which was
sufficient to generate a spike. Depending on the extent of its gap
junction connectivity, model neuron 2 could either generate a spike or
responded with a sub-threshold voltage to the spike event in model
neuron 1. The intensity of stimulation current was set to zero in this
example. The strength of functional coupling was
(10 fold
higher as compared to the baseline network model, to compensate for the
lack of noisy stimulation current).

Just as synaptic connectivity is determined both by the number of connecting
neurons (topological aspect) and the strength of synaptic weight between a
particular pair of neurons (functional aspect), the electrical connectivity is
determined by the number of neurons that establish gap junctions with a
particular neuron (topological aspect, parameterized by the number of neighbors
Z in our model), and the unitary gap junction conductance and the number of gap
junctions between a given pair of neurons (functional aspect, parameterized by
the strength of coupling in our model). How
do the topological and the functional aspects of gap junction connectivity
determine the patterns of neuronal activity? Figure 2 shows the dependence of
network-averaged firing rate on the strength of coupling
() and on the intensity of stimulation current
(Dn). For low topological connectivity
(Z = 4, left plot) the strength of coupling only weakly
affected the dependence of the firing rate on the stimulation intensity. On the
other hand, in highly interconnected network (Z = 24, right
plot) the “firing rate-driving noise” relation was critically shaped
by the value of parameter . The critical
intensity of stimulation current for which the transition from low to high rate
firing occurred generally moved to the left for higher
(note, however, a “gap” for an intermediate
coupling strength , indicating a
transition from the regime of stimulus-driven activity to the regime in which
spiking actively spreads through the network via strong gap junctions). Thus,
for a given strength of functional coupling (gap junction
conductance) the topological coupling (number of neighbors) could set the
context for the intensity of noisy synaptic-like stimulation that was needed in
order to drive the network to relatively high firing rates (>20 Hz). In
particular, in the regime of weak coupling (small
), stronger topological connectivity (high Z) tended to
suppress stimulus-driven activity over a wide range of stimulation intensities
(compare, e.g., left and right plots in Figure 2 for ). The implication
of this regime (very weak expression of gap junctions, as appears in
“healthy” mature cortex) to the regulation of collective activity is
discussed below.

Figure 3A shows the
dependence of mean and standard deviation of averaged (over network) neuronal
firing rate on the intensity of the driving current,
, for different patterns of topological connectivity.
While for sufficiently intense inputs, intense gap junction connectivity (more
pair-wise connections between any 2 model neurons) led to more intense and more
regular firing (Figure 3A),
this effect was opposite for the low driving current (described by lower
). Effect of the connectivity pattern (Z) on the firing
rate inverted at . Thus, for weak
driving current the higher number of topological connections between the neurons
appeared to suppress the stimulus-driven spiking (Figure 3A). As was explained in [25] this
suppressing effect is attributed to the faster relaxation of sub-threshold
perturbations in a presence of gap junction coupling between neurons that allows
depolarizing currents to “escape” from perturbed neurons to its
neighbors. Indeed, for relatively weak electrical coupling strength and large
number of neighbor neurons, the effective leak conductance of a neuron is
increased; the rheobase (a minimal constant current that is needed to evoke
spiking) is increased as well. Under these conditions, a perturbation to a given
neuron is effectively shared by all neighbor neurons and the effect of the
perturbation is diluted, reducing the chances to generate action potential. This
mechanism promotes the stability against relatively modest perturbations (below
the rheobase which is determined by the local gap junction connectivity). In
contrast, for supra-rheobase perturbations, the presence of gap junctions aids
in activity propagation and network synchronization.

The stabilizing effect of the strong topological coupling in the regime of weak
driving current had additional implications for collective neuronal activity;
faster decay of sub-threshold perturbations was expected to result in more
failures in signal propagation thus increasing the spike number disorder measure
(see Methods). We verified this hypothesis by computing the (binned) intensity
of collective activity for different patterns of connectivity and stimulation
current intensity (Figure
3B). For weak stimulation (), stronger
coupling between the model neurons significantly increased the spike number
disorder measure (Figure
3B). In contrast, the spike number disorder measure remained nearly
constant as a function of topological coupling when the network was driven by
high intensity current () (Figure 3B). Further visual
inspection (Figure 3C) shows
that in the weak topological connectivity regime (Z = 4,
left panel in Figure 3C)
increasing the intensity of stimulation gradually increased the firing rate of
model neurons while the collective activity remained largely asynchronous. By
contrast, in highly interconnected network (Z = 24, right
panel in Figure 3C) applying
the same driving current intensity first resulted in only minimal changes to the
network firing rate but as the input intensity increased led to high-frequency
synchronous bursts qualitatively similar to inter-ictal epileptiform
discharges.

These results demonstrate that under certain circumstances (weak functional
coupling, manifested physiologically as small unitary conductance) strong
topological connectivity by gap junctions can help down-regulate collective
neuronal activity. Stated in other words, in networks coupled by electrical
synapses with weak permeability of individual gap junctions, local structure of
topological connectivity can be a network stabilizing factor, an implication
which could have far reaching global effects when long-range chemical synaptic
interaction is explicitly added. Chemical synapses provide a way for fast,
long-distance neuronal communication; thus, local perturbations of electrical
activity can quickly spread and ignite the network. In the situation like this,
the local characteristics of gap junction connectivity can play a pivotal role
in defining the local response to perturbations. In what follows, we
investigated how local variations in topological connectivity of electrical
coupling and stimulation intensity (that may be interpreted as the strength of
perturbations due to long-range chemical synaptic communication) change local
and global dynamics in a network of model neurons coupled by gap junctions.

Hot-spots of intense activity are associated with weakly coupled
neurons

Epileptic activity often arises following traumatic brain injury (TBI) which can
impair synaptic connectivity and thus affect the local excitation/inhibition
balance [26], [27]. Given this causal role of synaptic connectivity
changes in the onset of seizures, we wanted to see whether the trauma-like
changes in gap junction connectivity would have any similar effect. To this end
we considered a simple “trauma scenario” in which the gap junction
connectivity in a predefined subnetwork ( block of neurons)
of highly interconnected network (Z = 24) was reduced
compared to the connectivity in the rest of the network. The reduction in the
connectivity inside the lesioned subnetwork was parameterized by
which corresponded to the probability to destroy an
existing connection between a pair of model neurons inside the lesioned block.
Thus, for all the neurons inside the square
block (total neurons) were
completely disconnected from each other, but retain their gap junction
connections with the “intact” neurons in the rest of the network.
Since we were interested in the effect that the structural change in gap
junction connectivity can have on the emerging activity, the intensity of the
stimulation current was kept constant throughout these simulations
().

As Figure 4 shows, lesioning
of gap junction connectivity at time T = 6 seconds led to
increase in spiking activity for model neurons in the traumatized subnetwork. In
Figure 4A, the time at
which the connectivity was lesioned and the extent of lesioned region are marked
with red line. Firing rate of model neurons in the traumatized subnetwork was
higher for larger lesions (higher values of in Figure 4B) and increased for
stronger disruption of gap junction connectivity inside the traumatized region
(higher value of plesion in Figure 4B). Interestingly, the firing rate of
the neurons with intact connectivity also increased (Figure 4C), which was largely attributed to
increase of activity of neurons near the border with traumatized region, but
propagation of high frequency activity through the intact network was not
supported (Figure 4A bottom
panel). Overall, following the lesion, the firing rate of lesioned neurons could
increase up to 10-fold as compared with the activity in the baseline model
before lesion (Figure 4B,C).
Thus, localized lesions of gap junction connectivity can create localized
hot-spots of intense activity.

A Raster plots showing the response of model neuronal
network to the localized lesion of gap junction connectivity. The extent
of lesion is parameterized by the probability
to destroy
a connection between a pair of model neurons in the predefined area
. The
timing of lesion is marked with red line. Top panel:
. Middle
panel: . Bottom
panel: . Other
parameters are: .
B Averaged firing rate
(meanS.E.M.) in
the lesioned subnetwork vs. the size of the lesioned subnetwork. Closed
squares: . Open
squares: . In all
cases . Firing rate was computed over the time window
of 20 seconds. C Averaged firing rate
(meanS.E.M.) in
the intact subnetwork, vs. the size of the lesioned subnetwork. Closed
squares: . Open
squares: . In all
cases . For comparison, dashed line is the averaged
neuronal firing rate in baseline conditions (model network with fully
intact connectivity). Firing rate was computed over the time window of
20 seconds. D Raster plot showing the response of a model
network with electrical and chemical synapses to the localized breach in
gap junction connectivity. Disruption to gap junction connectivity was
applied at time T = 2 seconds and is marked with
red line. Chemical synaptic connections were added to the model as
described in Methods.

To test if the model prediction is correct in the network with mixed synaptic and
electrical connectivity, we extended our model by including synaptic
interactions (see Methods for description of this simulation). Similar to the
previous simulations, the intact network displayed stable asynchronous activity.
Following the local lesion of gap junction connectivity, firing rates were
increased, activities of individual neurons became synchronized and were carried
to other (“healthy”) parts of the network, resembling seizure-like
dynamics (Figure 4D).

Adaptive increase in gap junction number can mitigate the response to mild
perturbations

We showed earlier that the localized lesion of gap junction connectivity could
result in a localized strong (up to 10-fold) increase in the firing rate of
affected neurons. Chemical synaptic signaling could in principle communicate
this increased firing rate to the other parts of the network and thus create a
perturbation of neuronal excitation there. Such increase of
“external” drive to a part of the network can be simulated in our
model by changing intensity of stimulation
current. The last can therefore be interpreted as increase in random synaptic
drive from the parts of the network that are not explicitly modeled here. Below
we will ask if a localized change in gap junction content may help to mitigate
the potentially pathological effect of such random input increase.

To address these questions, we considered a simple scenario in which model
neurons in the predefined sub-network (square block, total
number of perturbed neurons ) were subjected to
higher intensities of stimulation current as compared to the baseline input
intensity applied to the neurons in the rest of the network. The change of the
stimulus intensity was initiated at time (marked with red
dashed line in Figure 5),
and the intensity of the stimulation was gradually increased (Figure 5, bottom panel).
Overall, starting from the time of the initial perturbation, the background
current intensity increased 5-fold compared to its baseline value (from
to ). To address the
possible regulatory effect of gap junction connectivity, at time
(marked with blue dashed line in Figure 5, ) new gap junction
connections were formed between the model neurons in the perturbed sub-network,
such that the probability to create a new gap junction between a pair of
previously unconnected neurons was .

A Color panel is the surface plot of firing rate (averaged
over non-overlapping bins of 100 ms and over all model neurons) vs. the
probability to
establish a new gap junction between a pair of previously unconnected
neurons from the affected area (,). Color
code is blue for low firing rate and red for high firing rate.
Horizontal axis is simulation time ([0–10] seconds).
Background noise intensity for the
set of model
neurons was perturbed at (dashed
red line through Panels B,C), and progressively increased to achieve
5-fold higher values at time 10 seconds (scale bar in lowest panel). Gap
junction connectivity for model
neurons was increased (as specified by ) at time
(dashed
blue line through Panels B,C). B Raster plot of
network's activity for . Other
parameters: .
C Third panel: Raster plot of network's activity
for . Other parameters:
.

In Figure 5A, the firing rate
(average over non-overlapping bins of 100 ms and over all neurons in the
network) is shown for different values of and different
stimulation current intensities. As expected, higher stimulus intensity always
led to more intense neuronal firing. For relatively mild perturbation, its
effect on the firing rate could be offset by adding more gap junctions between
previously unconnected model neurons in the perturbed sub-network (Figure 5B, middle section).
However, if the perturbation of neuronal excitation was further increased, a
more interconnected sub-network could become a focus of intense activity that
spread through the entire network (Figure 5B, rightmost section). When fewer new gap junction
connections were added, it had almost no stabilizing effect of the network
firing rate (Figure 5C).
Thus, sufficiently strong increase in gap junction connectivity in response to
the increased levels of activity (as occurs in experimental models of evoked
seizures) has two facets – it can either bring the activity back to the
normal regime or contribute to seizure initiation. Which one of the routes is
ultimately taken depends on the strength, pattern and duration of the
perturbation applied to the network.

Because the basic mechanism by which gap junctions affect network stability is a
change in the value of rheobase current (and thus a change in the excitability
of individual neurons), the results presented in Figure 5 could be a consequence of reduced
excitability of model neurons after adding new gap junctions. Alternatively,
both altered neuronal excitability and change of the efficiency with which a
perturbation of activity can propagate through the network with different
density of connections might be involved. To test this question we performed the
following test. Same network (as the one studied in Figure 5) was considered, and the same
perturbation (time of perturbation marked with red dashed line in Figure 6), was applied to the
network as in Figure 5. At
time (marked with blue dashed line in Figure 6, ), pairs of model
neurons in perturbed sub-network were picked at random with probability
, and membrane conductance of each one of these model
neurons was increased by (which corresponds
to the conductance of individual gap junction in our model). In this way, we
obtained a network in which pattern of gap junction connectivity was the same
one as in the baseline “healthy” network, but membrane conductances
of neurons in perturbed sub-network were increased by the same amount as would
have occurred if actual new gap junctions were added. This allowed us to
separate the effects of increased membrane conductance from the effects of
increased connectivity.

A Color panel is the surface plot of firing rate (averaged
over non-overlapping bins of 100 ms and over all model neurons) vs. the
probability to
increase the leak conductance by in each
one of the model neurons from the affected area
(,) that did
not previously share gap junction connection. Color code is blue for low
firing rate and red for high firing rate. Horizontal axis is simulation
time ([0–10] seconds). Background noise intensity
for the
set of model
neurons was perturbed at (dashed
red line through Panels B,C), and progressively increased to achieve
5-fold higher values at time 10 seconds (scale bar in lowest panel).
Membrane leak conductance for model
neurons was increased (as specified by ) at time
(dashed
blue line through Panels B,C). B Raster plot of
network's activity for . Other
parameters: .
C Third panel: Raster plot of network's activity
for . Other parameters:
.

A brief glance at Figure 6
discloses the dramatic effect that the gap junction connectivity (rather than a
mere increase in membrane conductance) has on network dynamics. When leak
conductance of neurons was increased by the same amount as would have occurred
if actual gap junctions were added to the network, the firing rate was
stabilized for a wide range of perturbation intensities. Scenarios that led to
epileptic like outbreak of activity in the model with actual gap junctions
present now resulted in stable dynamics (compare firing rates in upper right
corner of top panel in Figures
5,6). Thus,
indeed, stabilizing effect of gap junctions can be attributed to the increase in
membrane conductance (decrease in input resistance) making neurons less
excitable. In space of the model parameters, gap junctions stabilize network
dynamics for low connectivity patterns (low ) or weak external
perturbations (low Dn). In the limit of high
and high Dn (top right
corner in the Fig. 5A),
however, gap junctions breached dynamical stability by means of higher
connectivity.

Discussion

As was first shown by Kepler et al. [28], resistive coupling can significantly affect the
frequency of a neural oscillator in a way that depends on several parameters, such
as the strength of coupling (conductance), the state of a neuron to which the given
neural oscillator is coupled, and the sub-threshold dynamics of the oscillator. Our
observations extend the conclusions of Kepler et al. [28] to networks of noise-driven
model neurons with realistic firing properties, and highlight another aspect
critical for collective activity – the topological connectivity. We identified
a special regime in which strong topological connectivity can dramatically suppress
the collective noise-driven activity. This occurred because topologically strong
(large number of contacts) but functionally weak (relatively weak individual
contacts) electrical connections reduced input resistance of the model neurons and,
therefore, enforced fast relaxation of sub-threshold excitation, thus weakening the
neuronal responsiveness to external stimulation [25]. However, the primary effect of
increase in gap junction connectivity can be described as membrane conductance
increase only under assumption that other neurons are far enough from spiking
threshold. Since currents escape through gap junction to other neurons, not to the
extracellular space, once many neurons are close enough to the spiking threshold,
effect of gap junctions reverts and starts to mediate firing rate increase. Thus, in
spatially extended networks of resistively coupled neurons driven by fluctuating
current, the spatial profile of the current and the connectivity of the network
(both its topological and functional aspects) can dramatically affect the emerging
collective activity.

Our present study was restricted to investigations of dynamics in a network of
neurons coupled only by gap junctions. Effects of chemical synaptic signaling were
all lumped into the phenomenological “stimulation current”. This
approximation allowed us to identify the contribution of gap junction coupling to
the maintenance of dynamical stability in a network. In reality though, fast
long-range signaling by chemical synapses can significantly affect the stability of
the network dynamics, either by increasing the excitation (for example, by strong
recurrent excitation) to the point of seizure outbreak, or by reducing the overall
excitation through activation of the inhibitory interneurons. Existing models of
epileptogenesis rely extensively on chemical synaptic interaction, and
experimentally, epileptic seizures are eliminated by blocking chemical synaptic
transmission. The sum effect of explicitly adding chemical synaptic transmission to
our computational model would likely depend on the topology of synaptic connectivity
and its relation to the local topology of gap junction connectivity; the relevant
physiological data are not available at the moment. In the meantime, we note that
the basic mechanism by which gap junctions can contribute to the maintenance of
dynamical stability in the network does not depend on synaptic connectivity,
therefore, our model predictions should still stand for the networks with mixed
electrical and chemical connectivity.

A significant body of data from in-vitro experiments and in-vivo measurements
suggests that electrical coupling between neurons by means of gap junctions may play
important role in information processing [29], [30], but also in the initiation and
escalation of epileptic seizures [16]. Computational models
suggest that gap junctions could be responsible for the generation of fast
oscillations that precede seizures [31]. In physiological preparations, the expression of gap
junctions was increased following the experimentally induced model of epilepsy [20]; thus,
seizure in itself could be responsible for the increased gap junction content. Thus,
while there is a consensus regarding the importance of gap junction communication in
epilepsy, the causal link between enhanced gap junction expression and the emergence
of seizures is still missing. Our studies suggest a possible new role for neuronal
gap junctions that contrast a common view according to which this mode of
communication is a pure epileptogenic factor promoting seizure activity. We posit
that in experimental models of epileptic seizures, an initial increase in gap
junction content between the neurons could represent an adaptive response by which
the network tries to reduce the effects of transient aberrant neuronal activity. For
relatively weak and transient perturbations of activity, such adaptive response can
help to suppress the undesirable hyper-excitation; however, if the perturbation of
activity is too strong and/or too persistent, an increase in gap junction content
will lead to the escalation of seizure (Figure 7).

Figure 7. Schematic presentation of the effect that topological gap junction
connectivity can have on the regulation of activity in networks that are
prone to seizing.

For mild perturbation of activity (low noise intensity, blue region), an
increase in gap junction connectivity can offset the effect of perturbation
and restore low firing rate, thus suppressing potential seizures. On the
other hand, for strong perturbation (strong noise intensity, red region), an
addition of gap junctions is likely to result in seizure escalation.

In the present study, we described neurons as one-compartmental entities, omitting
the effects of dendritic integration altogether. While this assumption probably
reflects fairly well the dynamics of electrotonically compact neurons, in other
instances (such as for example is the case with pyramidal neurons in hippocampus)
active dendritic conductances could profoundly affect the mechanisms of signal
integration and spike generation. In fact, it was recently shown that individual
dendritic branches of the same pyramidal neuron can operate quite autonomously with
respect to the integration of synaptic signal; thus, pyramidal neurons are
conceptually similar to the two-layer neural network, with dendritic layer
processing synaptic input and somatic level evaluating the results of dendritic
computation [32].
While these dendritic effects are not accounted for in our model, they would not
change our qualitative conclusions regarding the role of topological connectivity;
rather, the argument will become more “localized” pertaining now not to
the entire neuron but to its individual dendritic branches. Exactly how the
morphology of dendritic tree and the notion of “two-layer network”
interact with topological considerations related to gap junction connectivity
remains to be elucidated in further studies, using multi-compartmental modeling
techniques.

As we argued in the present study, the topological connectivity of gap junction
coupled neurons, as well as the intensity of background driving noise, can greatly
affect the emerging network dynamics. While local gap junction connectivity was
explicitly modeled in this study, much more spatially expanded synaptic inputs
including both local and long-range connections from other network neurons as well
as from neurons which were not explicitly modeled, was represented by background
driving noise. With this assumption in mind, the above argument suggests that in the
regime of weak gap junction coupling (as appears to be the situation in the
“healthy” mature cortex) synaptic and gap junction connectivity can be
delicately co-regulated to maintain physiologically normal activity. One point in
support of this hypothesis is the observation that the expression of gap junctions
is abundant in early development (when synaptic connections are not fully
established) but is dramatically reduced as the network matures [21]. It is
tempting to speculate that the relative expression of chemical vs. electrical
synapses is subject to homeostatic co-regulation [33]; however, the mechanisms of
this regulatory process at present remain unclear.

Although we focused here on the putative role of neuronal gap junctions in the
generation of seizures, the conclusions of our present study are not limited to
neuronal networks, but rather can be applied to any spatially extended excitable
system with resistive coupling between its elements. Examples include networks of
cardiac cells, liver cells, or glial cells of the brain. In particular, for networks
of astrocytes (a major type of glial cells in the brain) the post-seizure increase
in the expression of gap junctions is well documented [19], suggesting that the same
down-regulatory effect as we described here may exist for glia. Gap junctions
between astrocytes are presumably needed for efficient transfer of information by
inter-cellular calcium waves [34]; however, an excessive expression of gap junctions in
astrocyte networks was implicated in seizure initiation [35], [36]. Thus, there is a need for
relatively rapid and flexible modulation of the effective gap junction connectivity
that would allow efficient propagation of information at the same time preventing
pathological dynamics. One solution was recently proposed by assuming that
astrocytic gap junctions have nonlinear transfer properties [37]. More studies are needed to
find out whether or not the same mechanism applies to neurons and what are its
implications regarding collective dynamics in neuronal networks. Meanwhile, the
results of the present study call to revise the current dogma regarding the purely
pathological role of neuronal gap junctions in epileptic activity.

Materials and Methods

Model Network

We described neuronal dynamics using well-studied Morris-Lecar model [23] that was
slightly modified by Prescott et al. [24] to account for correct
biological mechanisms of action potential generation. This simplified model
provides an optimal balance between realistic electrical properties of neuron
and computational performance that allows simulations of large-scale 2D
networks. Equations that describe the dynamics of model neuron indexed by a pair
are(1)(2)

(3)(4)

and are explained in details elsewhere [24]. The following parameter
values were used in the model: ,
, ,
, ,,
, ,
, ,
, .

We consider 2D networks of 50×50 model neurons, with periodic boundary
conditions. The term in Equation 1
contains information about the coupling of the neuron
to other neurons(5)

where is the strength of the gap junction coupling, and the
structure defines the connectivity of the network. Taking as an
example the regular 2 dimensional lattice (with Z = 4 model
neurons connecting to each model neuron), one has
(where is the Kronecker
delta). In this work, we usually considered regular networks with
Z = 4,8,12,20,24 peer connections per model neuron (Figure 1A for
Z = 4). In some simulations, the effect of adaptive change
in gap junction connectivity was tested by creating, in a predefined region,
additional connections that targeted neurons beyond the baseline connectivity
footprint.

The strength of inter-neuronal gap junction coupling (surface density of gap
junction conductance) was set to . The exact value
of gap junction conductance between a pair of biological neurons is hard to
estimate. Amitai et al. [38] estimate that in neocortical interneurons, overall
gap junction conductance per neuron (sum over all gap junction conductances to a
given neuron) could contribute as much as 50 to 70 percent of neuronal membrane
conductance. However, interneurons are known to be much more densely
interconnected by gap junctions. Since we do not specify the exact nature of the
neurons studied here, we made a modest assumption of individual gap junction
conductance density of , which is about
0.4 percent of neuronal leak conductance density
().

Activity in biological neuronal network usually arises either due to the synaptic
stimulation or as a result of “noise” (this term captures influences
such as changes in external milieu, stimulation from glial cells, etc.).
Combining realistic topologies of synaptic and gap junction connectivity in the
same model framework necessitates accounting for geometrical and morphological
aspects (such as the distribution of neurons in space and incorporating
dendritic trees), a knowledge that is not fully available at the moment. The
goal of the present study was to investigate the contribution of gap junction
connectivity to dynamical stability of neuronal ensemble with respect to
perturbations in neuronal stimulation. Given this goal, and the complexity
associated with the studies of realistic synaptic and gap junction connectivity
schemes, we capture the generic effect of synaptic stimulation by introducing,
for each model neuron, a non-specific stimulation current(6)

In Equation 6, and parameterize the
intensity of stimulation and the characteristic decay time
(), and is the mean level
of stimulation current. The stochastic variable is drawn from
temporally and spatially uncorrelated Gaussian
distribution(7)

Collective activity of pyramidal neurons in “healthy” cortex is
characterized by relatively low degree of synchrony and individual neuronal
firing rates of ∼5 Hz. Therefore, we tuned the parameters of stimulation
current to obtain the desired firing rates. With , the constant
current was sub-threshold and no spikes could be generated.
Higher firing rate could be obtained by increasing the intensity of the
“synaptic” stimulation, (Figure 1B). Thus, the
parameter represented deviations, due to the sporadic synchronous
events, from asynchronous condition (represented by
). The value of defines the
strength of activity perturbation, with higher corresponding to
stronger perturbation as observed, for example, in epileptic cortices.
Conceptually, Equation 6 is equivalent to the Langevin equation for
“colored” (correlated) noise [39]; thus, throughout the text we
will refer to interchangeably as “stimulation current”,
“background current” or “background noise”. It is
important to note that there is no feedback interaction between the
“stimulation current” (representing the effect of chemical synaptic
signaling) and the firing activity of neurons in our model network. The
potential of gap junctions to contribute to the maintenance of networks'
dynamical stability with respect to a perturbation (given by a certain value of
) can be deduced by comparing the mean firing rate in
different scenarios of gap junction connectivity. Thus, if for a given
, adding gap junctions to a network will reduce the mean
firing rate of model neurons, this will be taken as an evidence for
network-stabilizing role of gap junctions.

In a separate set of pilot simulations, we explicitly added chemical synaptic
connectivity to the model network in order to demonstrate how a localized breach
in dynamical stability that arises due to the lesioned gap junction connectivity
can ignite the network to exhibit seizure-like activity. To keep the discussion
simple, we considered a case when only fast excitatory AMPA synapses were added
to the network. Synaptic connectivity was globally random, meaning that each
neuron could establish synaptic connection with any other neuron in the network
with probability . Synaptic current
from individual synaptic connection was(8)

with (9)

and .

Analysis

The firing rate of model neurons was estimated by computing the number of action
potentials generated in a predefined time window, and then normalizing by the
window duration.

To characterize the extent to which the abundance of gap junctions in a given
neuron can affect the propagation of electrical activity, we used the following
procedure: First, for a preset time window T, a spike count
of each model neuron in the network was obtained. Then,
for each model neuron, we computed the averaged spike count of its topological
neighbors, . The “spike number disorder” is then defined
as(10)

with the average taken over all model neurons in the network. The measure
quantifies the normalized absolute deviation of activity
from the topological mean. For we set
.

Author Contributions

Conceived and designed the experiments: VV MP MB. Performed the experiments:
VV. Analyzed the data: VV MP MB. Wrote the paper: VV MP MB.